Influencing factors on depression among medical staff in Hunan province under ordinal regression analysis
10.3760/cma.j.issn.0254-6450.2012.11.003
- VernacularTitle:应用Ordinal回归分析湖南省医务人员抑郁发生的影响因素
- Author:
Zhi-Yu LIU
1
;
Meng ZHONG
;
Yan HAI
;
Qi-Yun DU
;
Ai-Hua WANG
;
Dong-Hua XIE
Author Information
1. 湖南省妇幼保健院保健信息部
- Keywords:
Depression;
Influencing factors;
Ordinal regression;
Medical staff
- From:
Chinese Journal of Epidemiology
2012;33(11):1115-1118
- CountryChina
- Language:Chinese
-
Abstract:
Objective To understand the situation of depression and its related influencing factors among medical staff in Hunan province.Methods Data were collected through random sampling with multi-stage stratified cluster.Wilcoxon rank sum test,Kruskal-Wallis H test and Ordinal regression analysis were used for data analysis by SPSS 17.0 software.Results This survey was including 16 000 medical personnel with 14 988 valid questionnaires and the effective rate was 93.68％.Results from the single factor analysis showed that factors as:level of the hospital grading,gender,education background,age,occupation,title,departments,the number of continue education,income,working overtime every week,the frequency of night work,the number of patients treated in the emergency room etc.,had statistical significances (P＜0.05).Data from ordinal regression showed that the probabilities related to depression that clinicians and nurses suffering from were 1.58 times more than the pharmacists (OR=1.58,95％ CI:1.30-1.92).The probability among those whose income was less than 2000 Yuan/month was 2.19 times of the ones whose earned more than 3000 Yuan/month (OR=2.19,95％CI:2.05-2.35).The higher the numbers of days with working overtime every week,the frequencies of night work,and the numbers of patients being treated at the emergency room,with more probabilities of the people with depression seen in our study.Conclusion Depression seemed to be common among doctors and nurses.We suggested that the government need to increase the monthly income and to reduce the workload and intensity,lessen the overworking time,etc.
